TY - GEN
T1 - RespWatch
T2 - 6th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2021
AU - Dai, Ruixuan
AU - Lu, Chenyang
AU - Avidan, Michael
AU - Kannampallil, Thomas
N1 - Funding Information:
This research was supported in part by a grant-in-aid from the Division of Clinical and Translational Research of the Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri; and in part, by the Healthcare Innovation Lab and the Institute for Informatics at BJC HealthCare and Washington University School of Medicine. Special thanks to Linda Yun, who was the study coordinator and helped to collect all the data.
Publisher Copyright:
© 2021 ACM.
PY - 2021/5/18
Y1 - 2021/5/18
N2 - Respiratory rate (RR) is a physiological signal that is vital for many health and clinical applications. This paper presents RespWatch, a wearable sensing system for robust RR monitoring on smartwatches with Photoplethysmography (PPG). We designed two novel RR estimators based on signal processing and deep learning. The signal processing estimator achieved high accuracy and efficiency in the presence of moderate noise. In comparison, the deep learning estimator, based on a convolutional neural network (CNN), was more robust against noise artifacts at a higher processing cost. To exploit their complementary strengths, we further developed a hybrid estimator that dynamically switches between the signal processing and deep learning estimators based on a new Estimation Quality Index (EQI). We evaluated and compared these approaches on a dataset collected from 30 participants. The hybrid estimator achieved the lowest overall mean absolute error, balancing robustness and efficiency. Furthermore, we implemented RespWatch on commercial Wear OS smartwatches. Empirical evaluation demonstrated the feasibility and efficiency of RespWatch for RR monitoring on smartwatch platforms.
AB - Respiratory rate (RR) is a physiological signal that is vital for many health and clinical applications. This paper presents RespWatch, a wearable sensing system for robust RR monitoring on smartwatches with Photoplethysmography (PPG). We designed two novel RR estimators based on signal processing and deep learning. The signal processing estimator achieved high accuracy and efficiency in the presence of moderate noise. In comparison, the deep learning estimator, based on a convolutional neural network (CNN), was more robust against noise artifacts at a higher processing cost. To exploit their complementary strengths, we further developed a hybrid estimator that dynamically switches between the signal processing and deep learning estimators based on a new Estimation Quality Index (EQI). We evaluated and compared these approaches on a dataset collected from 30 participants. The hybrid estimator achieved the lowest overall mean absolute error, balancing robustness and efficiency. Furthermore, we implemented RespWatch on commercial Wear OS smartwatches. Empirical evaluation demonstrated the feasibility and efficiency of RespWatch for RR monitoring on smartwatch platforms.
KW - deep learning
KW - hybrid model
KW - mobile sensing
KW - respiratory rate
KW - Smartwatch
UR - http://www.scopus.com/inward/record.url?scp=85107185887&partnerID=8YFLogxK
U2 - 10.1145/3450268.3453531
DO - 10.1145/3450268.3453531
M3 - Conference contribution
AN - SCOPUS:85107185887
T3 - IoTDI 2021 - Proceedings of the 2021 International Conference on Internet-of-Things Design and Implementation
SP - 208
EP - 220
BT - IoTDI 2021 - Proceedings of the 2021 International Conference on Internet-of-Things Design and Implementation
PB - Association for Computing Machinery, Inc
Y2 - 18 May 2021 through 21 May 2021
ER -